ArticleHuman vaccines & immunotherapeutics2026
Development and validation of a predictive model for high-risk immune-related adverse events in gastric cancer patients treated with ICIs.
Article in Human vaccines & immunotherapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Prediction of Immune Checkpoint Inhibitor-Induced Liver Injury in Patients with Gastrointestinal Cancer: Machine Learning Modeling for Time-Stratified Risk Assessment.Current oncology (Toronto, Ont.) · 2026Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Immune checkpoint inhibitors (ICIs) may cause immune-related adverse events (irAEs), ranging from mild to life-threatening. High-risk irAEs can lead to treatment discontinuation and higher mortality, though ICI-treated patients' death rate is under 5%. Currently, no reliable biomarkers predict irAEs' occurrence or severity. This study investigates the link between accessible biomarkers and high-risk irAEs in gastric cancer patients on ICIs, as well as to develop and assess a predictive model for such events. Data were collected from patients with gastric cancer who received ICIs therapy between May 2020 and March 2025. The incidence and risk factors associated with irAEs were analyzed using the chi-square test or the Mann-Whitney U test. Univariate and multivariate logistic regression analyses were conducted to develop a predictive model. This model was validated through 10-fold cross-validation and assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), calibration curves, and decision curve analysis. A total of 184 gastric cancer patients receiving ICIs therapy were enrolled in this study. The incidence of irAEs of any grade was 21.2%, while the incidence of grade ≥3 irAEs was 12.5%. Multivariate logistic regression analysis identified NLR-1 (
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